Functionally distinct regions of the locus control IgE or IFNγ level in addition to skin lesions.

Leishmaniasis, a disease caused by parasites of spp., endangers more than 1 billion people living in endemic countries and has three clinical forms: cutaneous, mucocutaneous, and visceral. Understanding of individual differences in susceptibility to infection and heterogeneity of its pathology is largely lacking.

Structure vs. chemistry: Alternate mechanisms for controlling leaf microbiomes.

The analysis of phyllosphere microbiomes traditionally relied on DNA extracted from whole leaves. To investigate the microbial communities on the adaxial (upper) and abaxial (lower) leaf surfaces, swabs were collected from both surfaces of two garden plants, Rhapis excelsa and Cordyline fruticosa. Samples were collected at noon and midnight and at five different locations to investigate if the phyllosphere microbial communities change with time and location.

Short-range contributions of local sources to ambient air.

Recent developments in aerobiology have enabled the investigation of airborne biomass with high temporal and taxonomic resolution. In this study, we assess the contributions of local sources to ambient air within a 160,000 m tropical avian park (AP). We sequenced and analyzed 120 air samples from seven locations situated 160 to 400 m apart, representing distinct microhabitats.

Population-specific positive selection on low CR1 expression in malaria-endemic regions.

Complement Receptor Type 1 (CR1) is a malaria-associated gene that encodes a transmembrane receptor of erythrocytes and is crucial for malaria parasite invasion. The expression of CR1 contributes to the rosetting of erythrocytes in the brain bloodstream, causing cerebral malaria, the most severe form of the disease. Here, we study the history of adaptation against malaria by analyzing selection signals in the CR1 gene.

Vertical stratification of the air microbiome in the lower troposphere.

The troposphere constitutes the final frontier of global ecosystem research due to technical challenges arising from its size, low biomass, and gaseous state. Using a vertical testing array comprising a meteorological tower and a research aircraft, we conducted synchronized measurements of meteorological parameters and airborne biomass ( = 480) in the vertical air column up to 3,500 m. The taxonomic analysis of metagenomic data revealed differing patterns of airborne microbial community composition with respect to time of day and height above ground.

Population-specific adaptation in malaria-endemic regions of asia.

Evolutionary mechanisms of adaptation to malaria are understudied in Asian endemic regions despite a high prevalence of malaria in the region. In our research, we performed a genome-wide screening for footprints of natural selection against malaria by comparing eight Asian population groups from malaria-endemic regions with two non-endemic population groups from Europe and Mongolia. We identified 285 adaptive genes showing robust selection signals across three statistical methods, iHS, XP-EHH, and PBS.

Experimental parameters defining ultra-low biomass bioaerosol analysis.

Investigation of the microbial ecology of terrestrial, aquatic and atmospheric ecosystems requires specific sampling and analytical technologies, owing to vastly different biomass densities typically encountered. In particular, the ultra-low biomass nature of air presents an inherent analytical challenge that is confounded by temporal fluctuations in community structure. Our ultra-low biomass pipeline advances the field of bioaerosol research by significantly reducing sampling times from days/weeks/months to minutes/hours, while maintaining the ability to perform species-level identification through direct metagenomic sequencing.

Performance of model-based multifactor dimensionality reduction methods for epistasis detection by controlling population structure.

Background: In genome-wide association studies the extent and impact of confounding due to population structure have been well recognized. Inadequate handling of such confounding is likely to lead to spurious associations, hampering replication, and the identification of causal variants. Several strategies have been developed for protecting associations against confounding, the most popular one is based on Principal Component Analysis.

Taxonomic composition and seasonal dynamics of the air microbiome in West Siberia.

Here, we describe taxonomical composition, as well as seasonal and diel dynamics of airborne microbial communities in West Siberia. A total of 78 airborne biomass samples from 39 time intervals were analysed, within a temperature range of 48 °C (26 °C to - 22 °C). We observed a 5-170-fold decrease in DNA yield extracted from the airborne biomass in winter compared to summer, nevertheless, yielding sufficient material for metagenomic analysis.

Microbial communities in the tropical air ecosystem follow a precise diel cycle.

The atmosphere is vastly underexplored as a habitable ecosystem for microbial organisms. In this study, we investigated 795 time-resolved metagenomes from tropical air, generating 2.27 terabases of data.

Confounding of linkage disequilibrium patterns in large scale DNA based gene-gene interaction studies.

Background: In Genome-Wide Association Studies (GWAS), the concept of linkage disequilibrium is important as it allows identifying genetic markers that tag the actual causal variants. In Genome-Wide Association Interaction Studies (GWAIS), similar principles hold for pairs of causal variants. However, Linkage Disequilibrium (LD) may also interfere with the detection of genuine epistasis signals in that there may be complete confounding between Gametic Phase Disequilibrium (GPD) and interaction.

Male-specific epistasis between WWC1 and TLN2 genes is associated with Alzheimer's disease.

Systematic epistasis analyses in multifactorial disorders are an important step to better characterize complex genetic risk structures. We conducted a hypothesis-free sex-stratified genome-wide screening for epistasis contributing to Alzheimer's disease (AD) susceptibility. We identified a statistical epistasis signal between the single nucleotide polymorphisms rs3733980 and rs7175766 that was associated with AD in males (genome-wide significant p=0.

Complete Genome Sequence of Type Strain SGAir0031 Isolated from Tropical Air Collected in Singapore.

strain SGAir0031 () was isolated from tropical air samples collected in Singapore. Its genome was assembled using short reads and single-molecule real-time sequencing, comprising one chromosome with 3.81 Mb and one plasmid with 32 kb.

A cautionary note on the impact of protocol changes for genome-wide association SNP × SNP interaction studies: an example on ankylosing spondylitis.

Genome-wide association interaction (GWAI) studies have increased in popularity. Yet to date, no standard protocol exists. In practice, any GWAI workflow involves making choices about quality control strategy, SNP filtering, linkage disequilibrium (LD) pruning, analytic tool to model or to test for genetic interactions.

High-density mapping of the MHC identifies a shared role for HLA-DRB1*01:03 in inflammatory bowel diseases and heterozygous advantage in ulcerative colitis.

Genome-wide association studies of the related chronic inflammatory bowel diseases (IBD) known as Crohn's disease and ulcerative colitis have shown strong evidence of association to the major histocompatibility complex (MHC). This region encodes a large number of immunological candidates, including the antigen-presenting classical human leukocyte antigen (HLA) molecules. Studies in IBD have indicated that multiple independent associations exist at HLA and non-HLA genes, but they have lacked the statistical power to define the architecture of association and causal alleles.

Practical aspects of genome-wide association interaction analysis.

Large-scale epistasis studies can give new clues to system-level genetic mechanisms and a better understanding of the underlying biology of human complex disease traits. Though many novel methods have been proposed to carry out such studies, so far only a few of them have demonstrated replicable results. Here, we propose a minimal protocol for genome-wide association interaction (GWAI) analysis to identify gene-gene interactions from large-scale genomic data.

Genome-wide association interaction analysis for Alzheimer's disease.

We propose a minimal protocol for exhaustive genome-wide association interaction analysis that involves screening for epistasis over large-scale genomic data combining strengths of different methods and statistical tools. The different steps of this protocol are illustrated on a real-life data application for Alzheimer's disease (AD) (2259 patients and 6017 controls from France). Particularly, in the exhaustive genome-wide epistasis screening we identified AD-associated interacting SNPs-pair from chromosome 6q11.

Genetic regulation of immunoglobulin E level in different pathological states: integration of mouse and human genetics.

Immunoglobulin E (IgE) first evolved in mammals. It plays an important role in defence against helminths and parasitic infection and in pathological states including allergic reactions, anti-tumour defence and autoimmune diseases. Elucidation of genetic control of IgE level could help us to understand regulation of the humoral immune response in health and disease, the etiology and pathogenesis of many human diseases, and to facilitate discovery of more effective methods for their prevention and cure.

A robustness study of parametric and non-parametric tests in model-based multifactor dimensionality reduction for epistasis detection.

Background: Applying a statistical method implies identifying underlying (model) assumptions and checking their validity in the particular context. One of these contexts is association modeling for epistasis detection. Here, depending on the technique used, violation of model assumptions may result in increased type I error, power loss, or biased parameter estimates.

An efficient algorithm to perform multiple testing in epistasis screening.

Background: Research in epistasis or gene-gene interaction detection for human complex traits has grown over the last few years. It has been marked by promising methodological developments, improved translation efforts of statistical epistasis to biological epistasis and attempts to integrate different omics information sources into the epistasis screening to enhance power. The quest for gene-gene interactions poses severe multiple-testing problems.

Lower-order effects adjustment in quantitative traits model-based multifactor dimensionality reduction.

Identifying gene-gene interactions or gene-environment interactions in studies of human complex diseases remains a big challenge in genetic epidemiology. An additional challenge, often forgotten, is to account for important lower-order genetic effects. These may hamper the identification of genuine epistasis.